Listen to Yourself: is it a real pattern?

I recently came across an article presenting “Listen to Yourself” pattern as a solution to the dual-write problem in event-driven systems. I’ve never heard about it before, so I became curious to research it.

Dual-write problem#

In software engineering, the dual-write problem is clearly defined.
It occurs when writing to a database and publishing a message to a broker must be atomic to avoid inconsistency. The established solution is the Transactional Outbox pattern, which offers two options for message relay: either separate poller process or log tailing, leveraging native RDBMS CDC mechanisms. It was described by C. Richardson (2019) — who notably didn’t mention “Listen to Yourself” approach in his (patterns collection), though he did comment on O. Shopen’s early Medium (article) introducing the term.

I researched the concept further, and below is a brief overview of how several community authors interpret this “pattern.”

Review on “Listen to Yourself”#

O. Shopen (2017). Referred to “Listen to Yourself” as a solution for a case where: a service (A) needs to commit a local transaction and invoke a legacy service (B), which commits its own local transaction, all atomically. In this case, it is suggested (A) to send an event to a streaming platform, the same service (A) listens to the same topic and commits its local transaction, the same does service (B). In case of failures in either service, a “compensating topic” is reserved.
While preserving the “listen to yourself” idea, this approach achieves eventual consistency between the two services but requires both consumers to be independent by design.

D. Comartin (2024). He describes a service (A) that instantly publishes an event to a stream and later consumes it from the same topic to perform actual processing and update its local database. “Listening to itself” simply means offloading heavy processing to the same service instances, avoiding a separate consumer tier, and reducing response time to the caller, which is useful when long-running tasks risk exceeding external timeouts (e.g., third-party webhooks). While claiming to solve a consistency problem, it effectively only solves “a guaranteed processing problem”.

W. Waldron (2024). Describes a service that receives a command from a user’s fitness tracker requiring heavy processing across multiple downstream systems. The command is immediately converted into a Kafka message, and once Kafka confirms receipt, the service returns a response to the user - deferring all complex processing to a later stage. In this setup, the event is safely persisted in Kafka, yet downstream consumers have no guarantee that the subsequent DB write actually succeeded. Therefore, it’s difficult to claim that this approach truly resolves the dual-write problem.

After all, I failed to find any canonical book that formally framed it as a pattern.

My take on “Listen to Yourself”#

Listening to yourself doesn’t fix dual-writes or reduce latency beyond what any event-first async design offers. Its only real gain is simpler ops and code locality - useful for MVPs or small teams, but at scale a separate consumer tier feels cleaner. Transaction Outbox solves this problem, but does not come without drawbacks, as database transaction usually takes time measured in tens/hundreds of ms to complete (depends on isolation level, indexing, and SSD disk latency), and hence under heavy load creates significant pressure on the database server. However, if an immediate response to the caller is not strictly required, this logic can be offloaded to a downstream consumers, while caller receives immediate response, say, with message “Job is accepted”.